DYNAMIC OPTIMIZATION USING ADAPTIVE GRID REFINEMENT WITH WAVELETS TRANSFORM in PYTHON

Resumen

The dynamic optimization is a useful mathematical tool to find the optimal solution ofproblems where a significant variability with the time of the present variables is observed. Thesolution to these problems may become difficult in some cases, such as problems with temporalconstraints or changes in the solution profile over time. In such cases, both the quality of thesolution and the computational cost can be improved. This work aims to develop an adaptivenumerical method, based on wavelets analysis, to solve dynamic optimization problems. Themethod was developed in the Python programming language, using the Gekko package. Twocases of dynamic optimization were solved and the results showed that the computational costcould be significantly reduced.